Master of Technology in Knowledge Engineering

Summary

The Master of Technology in Knowledge Engineering programme (MTech KE) is best suited for today's busy IT professionals and domain experts with existing career commitments who are seeking to build systems that use data-driven techniques (e.g. machine learning, business analytics) to automate processes usually performed by human beings, or else, to provide relevant data and insights for better decision-making.

Individuals who are strong in computer science subjects including data mining, algorithms and intelligent systems stand to benefit the most from this programme.

This programme is jointly offered by NUS-ISS, the Department of Electrical & Computer Engineering (Faculty of Engineering) and NUS School of Computing.

Application for the January 2018 intake will open on 1 February 2017.
Next Intake: Jan 2018
Duration: Full-time 1.5 years (3 semesters)

Part-time 2.5 years (5 semesters)
Application Deadline:
  • 31 Aug 2017 (Overseas applicants)
  • 15 Sep 2017 (Local applicants)

Fees

Singaporeans

Full Time
Subtotal (per semester) S$4,976.50 
$4,700.00 - Semester Tuition Fee
$276.50  - Semester Miscellaneous Fee
Part Time
Subtotal (per semester) S$2,514.25 
$2,350.00 - Semester Tuition Fee
$164.25  - Semester Miscellaneous Fee

Singapore Permanent Residents

Full Time
Subtotal (per semester) S$6,851.50 
$6,575.00 - Semester Tuition Fee
$276.50  - Semester Miscellaneous Fee
Part Time
Subtotal (per semester) S$3,464.25 
$3,300.00 - Semester Tuition Fee
$164.25  - Semester Miscellaneous Fee

International Students with Service Obligation

Full Time
Subtotal (per semester) S$10,326.50 
$10,050.00 - Semester Tuition Fee
$276.50  - Semester Miscellaneous Fee
Part Time
Subtotal (per semester) S$5,189.25 
$5,025.00 - Semester Tuition Fee
$164.25  - Semester Miscellaneous Fee

International Students without Service Obligation

Full Time
Subtotal (per semester) S$18,551.50 
$18,275.00 - Semester Tuition Fee
$276.50  - Semester Miscellaneous Fee
Part Time
Subtotal (per semester) S$9,314.25 
$9,150.00 - Semester Tuition Fee
$164.25  - Semester Miscellaneous Fee

Programme Details

  • Overview
  • Modules
  • Projects & Internships
  • Timetable & Exams
  • Fees & Loans
  • Admission & Application
  • Career Pathways

The MTech KE programme emphasises concepts, techniques and methods of Intelligent Systems, and their application to the development of data analytics applications.

The programme will equip you with the essential knowledge and practical experience to become a knowledge engineering and data analytics specialist, and lead the development of intelligent systems to provide effective and optimal business solutions for your organisation.

Learning outcomes:

  • Apply Intelligent Systems concepts, techniques and methods to solve typical business problems
  • Lead the development of Intelligent Systems using existing tools and techniques, including Prediction, Forecasting, Classification, Clustering and Optimisation
  • Design and customise algorithms to solve complex business problems and create strategic advantage

Recognition:

  • Top student is awarded the SPH Medal and Prize
  • Best Project Prize

MTech KE candidates must successfully complete the following course components to be awarded the degree:

  • Core Courses - Pass 4 compulsory Core Courses
  • Basic Elective Courses - Pass 8 Basic Elective Courses
  • Advanced Elective Courses - Pass 3 Advanced Elective Courses
  • Team-based Internship or Off-site Project

Core Courses (Compulsory)

KE4102 Intelligent Systems and Techniques for Business Analytics

This module provides the foundation for smart nation engineers to design and build strategically important Intelligent Systems by integrating expert systems techniques, data-driven methods, and machine learning algorithms. You will gain practical skills and knowledge in rule-based systems, search techniques, knowledge representation, reasoning, statistical and machine learning.
Pre-requisites: Nil

 

KE5106 Data Warehousing for Business Analytics

in this course,you will learn that Data Warehousing is an important preparatory process in the development of data-driven intelligent systems. In the first part of this course, you will learn the fundamental principles and practices of Data Warehousing, as well as gain an in-depth understanding of Relational Database Management Systems, SQL, OLAP, data visualisation and dashboards. In the second part of this course, you will learn about handling unstructured big data, web sourcing, and working with NoSQL databases.
Pre-requisites: Nil

 

KE5107 Data Mining Methodology and Methods

In this course,you will discover Data Mining as an important knowledge discover process in the development of intelligent systems for business analytics. It provides an in-depth coverage on the methodology and methods of data mining through practical examples and case studies. You will learn how to explore and prepare the data for predictive modelling; how dimension reduction techniques, segmentation and profiling can be used for long term strategy formulation. You will also learn how techniques such as market basket analysis, association mining and collaborative filtering can be used to mine user transactions and preferences to discover insights and make product related recommendations.
Pre-requisites: Nil

KE5108 Developing Intelligent Systems for Performing Business Analytics

The first part of this module discusses the major stages of modelling and development of intelligent systems, including problem understanding, problem modelling, system architecture and design, algorithm/technique selection, system development, and system fine-tuning. You will be introduced to some of the typical hybrid architectures of intelligent systems for problem solving, and some advanced techniques and algorithms in statistical machine learning. 

The second part of this module has an in-depth coverage of machine sensory systems through signal, image and video processing, analysis and application in intelligent systems. You will be exposed to the basics and advanced knowledge and algorithms that form the modus operandi behind how machine and robots perceive and process. There will also be real world scenarios and problem solving based on these newly acquired knowledge. 
Pre-requisites: KE4102 Intelligent Systems and Techniques for Business Analytics, KE5016 Data Warehousing for Business Analytics, KE5107 Data Mining Methodology, and Methods.

Basic Elective Courses

Choose any 4 from Knowledge Engineering Techniques

  • Computational Intelligence I
  • Computational Intelligence II
  • Text Mining
  • Case Based Reasoning
  • Sense Making and Insight Discovery
  • Machine Learning for Software Engineers 


Choose any 4 from these study areas:

Advanced IT Management

  • Managing IT Outsourcing & Subcontracting
  • Business Process Management
  • Agile Software Project Management
  • Advanced Software Estimation

Business Analytics Techniques

  • Campaign Management
  • Customer Relationship Management
  • Web Analytics
  • New Media and Sentiment Mining
  • Supply Chain Analytics
  • Service Analytics
  • Clinical Health Analytics
  • Geospatial Analytics

IT Infrastructure Technology

  • Information System Security
  • Cloud Computing
  • Internet of Things Technology

Requirements, Design & Construction

  • Software Requirements Engineering
  • Digital User Experience Design
  • Object Oriented Design Patterns
  • Architecting Software Solutions
  • Secure Software Life Cycle 

Software Development Platforms & Technologies

  • Enterprise .Net
  • Enterprise Java
  • Enterprise Integration
  • Mobile Wireless Solution Design

Technopreneurship & Innovation

  • Independent Work I
  • Independent Work II
  • Digital Innovation and Design
Click here for a detailed write-up on Basic Elective Courses





A central element of the MTech programme is the project module.

Student projects for MTech KE students extend over a period of eight months for full-time students and one year for part-time students. Full-time students are allowed to conduct their project as a team-based internship if desired. The expected commitment for the project is 60 man-days per team member.

Objectives

  • Acquire hands-on experience in defining and analysing the knowledge and data requirements of real-world business problems
  • Plan and strategise high-value intelligent systems projects to provide identifiable benefits to the internship company
  • Design, develop and implement intelligent systems through the effective use of knowledge and data engineering tools and techniques

Learning outcomes:

  • Conduct requirements analysis using a structured approach
  • Produce high-quality intelligent systems following industry best practices and methodologies
  • Proficient in the use of knowledge and data engineering tools and techniques to deliver optimal business value

Read more on Internships & Placement

Timetable & Exams for Full-time Students

KE_Full-time Timetable

Timetable & Exams for Part-time Students

KE_Part-time Timetable

Students are evaluated through a combination of course work, project work and examinations. All students are required to complete a three-hour examination for each core and elective course.

Students who fail a core course will be asked to withdraw. A minimum average grade across all examinations must be achieved to be awarded the degree. Students who do not fulfil the minimum requirements of the degree may be considered for the award of the postgraduate Diploma in Knowledge Engineering.

Exemptions for examinations may be granted for up to four basic electives, provided students have at least the equivalent of an NUS or NTU 2nd Upper Class Honours degree, and have passed the same or similar subjects at either a Masters or PhD level.





The fees above are for the Academic Year 2017 / 2018.

Fees are correct at time of posting and are subjected to changes without prior notice. The University reserves the right to alter the fees at any time. Fees for subsequent years are under review.

The Ministry of Education (MOE) of Singapore offers a tuition subsidy, known as the MOE subsidy. This subsidy will be administered automatically to eligible applicants. Read more about the eligibility guidelines.

For Singapore Citizens and Singapore Permanent Resident students, the fees quoted are subsidised by the Singapore government, through MOE, and are exclusive of GST. The applicable GST will be subsidised by the MOE as well.

What is Service Obligation?

The Service Obligation scheme is only applicable to international students.

The service obligation will require you to work in Singapore-based companies for 3 years upon graduation. Singapore-based companies refer to local and international companies that have a base in Singapore that is registered with the Accounting & Corporate Regulatory Authority (ACRA) as well as companies of such local and international companies registered with ACRA that are based overseas.

The MOE subsidy is not eligible to applicants without service obligation.

Read more about the Service Obligation scheme.

What Do Miscellaneous Fees Cover?

Miscellaneous fees are typically levied on items that are either not covered or partially covered by tuition fee and grant/subsidy. All students, whether registered on a full-time or part-time basis, are charged the mandatory miscellaneous fees. These are due at the same time as the tuition fees. These fees help defray the costs of student activity, health services and insurance, campus shuttle service and other services.

Any queries about fees and payment, please contact us at issfinance@nus.edu.sg.

Loans and Subsidies

Students who require financing for their tuition fees may apply for the following:

  • Tuition Fee Loan
  • SkillsFuture Mid-Career Enhancement Subsidy
  • SkillsFuture Credit

Read up more on the above loans and subsidies

Applicants must possess the following pre-requisites:

  • Bachelor's degree preferably in Science or Engineering and a grade point average of at least B
  • Proficiency in the English Language (written and spoken)*
  • Have passed an entrance test
    • Candidates who possess highly relevant Honours/Masters/PhD degrees may be granted entrance test waiver
    • ISS may, at its discretion, accept GRE general test in lieu of ISS entrance test in genuine cases (eg: a candidate lives in a country where ISS does not administer entrance tests or candidate had valid reasons that prevented him/her from attending the ISS entrance test when it was administered
    • A sample of the entrance test can be found here
  • Preferable have two years relevant working experience as an IT professional, eg: software developer, business analyst, or as a domain expert working in an arear where Knowledge Engineering can be applied
    • Candidates with highly relevant IT degrees, with consistently good academic records and good practical software development knowledge gained either through course work, course projects or professional IT certifications may be granted a work experience waiver
  • Have received a favourable assessment at admissions interview conducted by ISS

*English Language Proficiency

Applicants who graduated from universities where English is not the medium of instruction should submit TOEFL (Test of English as a Foreign Language) or IELTS (International English Language Testing System) score as evidence of their proficiency in the English language.

  • TOEFL : Paper-based test (580)
                 : Computer-based test (237)                 
                 : Internet-based test (85)
  • IELTS   : Result of 6.0

Note: Institution code of NUS-ISS for TOEFL is 2432

TOEFL and IELTS are only valid for five years after the test and the validity should not expire before the beginning of the application period for the coursework programme.

How to Apply

All applicants are required to apply online for our graduate coursework programme. Please note that you will be redirected directly to NUS’ Online Application System.

Step 1: Please click here to read the instructions before you proceed to apply online.

Step 2: Proceed to NUS’ Online Application System to apply

Step 3: Payment of Application Fee

Application Fee (non-refundable) – S$50.00 (inclusive of prevailing GST)

Note: Please pay your application fee (S$50.00) online via the Graduate Admission (GDA2) System. Print out a copy of the payment receipt and attach it with your application as proof of payment. Alternatively, you can also make payment in cheque or bank draft (made payable to “National University of Singapore”) and attach the Application Fee Form.

You should write your name, application number, identity/passport number and telephone numbers (home & office) on the reverse side of the cheque/bank draft.

Any further queries about payment, please contact us at issfinance@nus.edu.sg

Step 4: Submission Checklist

After submitting your application via the online Graduate Admission (GDA2) System, the completed online application form should be printed out and submitted to the School together with the supporting documents listed below. All documents which are to be submitted should preferably be in A4 size. All documents which are not in English must be accompanied by an official certified English translation. Omission of required information and documents may render the application void.

Supporting documents

  1. Online Application form (printed from the system after the online submission), duly declared and signed
  2. A recent coloured passport-sized photograph to be attached in the box provided in the Online Application form.
  3. MTech Form
  4. A copy of the following documents :
    • Degree scroll
    • Detailed result transcript of academic records from each university, polytechnic or college attended
    • A copy of professional certificates (if applicable)
    • A copy of the TOEFL/ IELTS score report
    • A copy of citizenship certificate, identity card, passport or documentary proof of permanent residence status, where applicable
    • A copy of Employment Pass for international applicants working in Singapore
  5. Testimonials from the employer (where relevant)
  6. The Cheque/ Bank Draft & Application Fee Form OR Proof of Payment of Application Fee (e.g. E-Receipt)

You will be asked to produce the originals for verification during enrolment (if your application is successful).

Please send the application form along with the supporting documents or submit them personally to:

Master of Technology Course Administrator
Institute of Systems Science
National University of Singapore
25 Heng Mui Keng Terrace
(off Pasir Panjang Rd)
Singapore 119615

IMPORTANT NOTE:
Completed application forms must be submitted with the supporting documents and an application fee of S$50.00. Incomplete applications (e.g. those with insufficient documents or have not satisfactorily completed their requirements for the bachelor's degree by the stipulated deadline of submission) and applications received after the closing date, will not be processed.

If you need to make changes or updates to your application after the online submission, please email isspostgrad@nus.edu.sg to inform us of the changes.

Step 5: Checking your status of application

After the application deadline, all received applications will be processed, and the online application status in the Graduate Admission System will be updated to "physical application verified" within 14 days.

Invitation to sit for the aptitude test & interview will be sent by email to the email address provided in the application form.

Results of your application will be made known to you through postal mail about 2 months after the application closing date. You can also check your application outcome results via the online admission system 2 months after the application closing date. If you do not hear from us two months after the deadline, please email isspostgrad@nus.edu.sg

Applicants who are unsuccessful in their application will need to submit a new application together with all the relevant supporting documents if they are interested to be considered for the programme again in the next intake.

Note:
Due to the large number of applicants seeking admission, we are sorry we will not be able to attend to enquiries on the status of applications or receipt of documents. If you are concerned about the delivery of your documents, you may wish to consider sending them via registered mail or courier.

Important: The University has not engaged any external agencies to undertake student recruitment on its behalf. Candidates interested in our graduate programmes are advised to apply directly to the University and not through any agents. Candidates who apply through agents will not have any added advantage in gaining admission and the University reserves the right to reject such applications without giving reasons.

Find your fit with new opened doors

There is opportunity in Singapore for most areas of IT. What you learn in terms of IT skills is not as important as what you do with it. It is the attitude and the ability to learn from mistakes, and to contribute back to the company that you work for that is likely to make more of a difference than specific IT skills.

There are two main paths for advancement in IT - either technical or management. Technical means you continue to deepen your technical area in a domain (such as system architecture, or software engineering, etc.) and you become an expert in those areas. The other is management, where you can focus on project management, outsourcing, etc.

Our internship companies often tell us that if we can give them good students as interns, it is very likely they will get a job offer at the end of the internship.

As an MTech KE graduate, you will be trained to become a knowledge engineering and data analytics specialist, leading the development of intelligent systems to provide smart business solutions for organisations.  

Career Prospects

  • Big Data Developer
  • Business Analyst
  • Business Analytics Consultant
  • Business Intelligence Engineer
  • Data Analyst
  • Data Scientist
  • Data Systems Specialist
  • Data Visualisation Engineer
  • Machine Learning Specialist
  • Research Engineer
  • Text Mining / Analytics Specialist

MTech alumni are pursuing their careers at these global organisations:

  • Accenture
  • Creative Technology
  • DBS Bank
  • Defence Science & Technology Agency
  • Deutsche Bank AG
  • Fuji Xerox Asia Pacific
  • HP Singapore
  • IBM Singapore
  • Infocomm Development Authority of Singapore
  • Inland Revenue Authority of Singapore
  • Jurong Port
  • Microsoft
  • Murex
  • NCS
  • NEC Asia Pacific
  • OCBC Bank
  • Revolution Analytics
  • Singapore Telecommunications
  • Standard Chartered Bank
  • Starhub
  • ST Electronics
  • Tata Consultancy Services

The NUS-ISS Career Services Office helps students to match jobs based on their skills and experience. There will be bi-yearly Career Fairs held for students and graduates to network with employers. However, successful employment will depend on the employers.

The average starting salary of an IT professional depends on the degree and your previous working experience. For fresh graduates with no work experience, the starting salary ranges from S$3,600 to S$3,800. Graduates with more than 3 years of work experience can expect a starting pay of S$4,000 and above.

The most important skill is to get the job done and be persistent. You need to be broad-based and the technology does not matter.

You can get some salary benchmarks from these sites:

Your Learning Journey

Term 1

4 CORE COURSES (Compulsory)

  • Intelligent Systems and Techniques for Business Analytics
  • Data Warehousing for Business Analytics
  • Data Mining Methodology and Methods
  • Developing Intelligent Systems for Performing Business Analytics
Term 2

8 BASIC ELECTIVE COURSES

  • Choose 4 courses from the Knowledge Engineering Techniques Group:
    • Computational Intelligence I
    • Computational Intelligence II
    • Text Mining
    • Case Based Reasoning
    • Sense Making and Insight Discovery
    • Machine Learning for Software Engineers
       
  • Choose 4 courses from other study groups
Term 3

3 ADVANCED ELECTIVE COURSES

Choose 3 courses offered by:

  • Institute of Systems Science
  • NUS School of Computing
  • Department of Electrical & Computer Engineering (Faculty of Engineering)
Term 4

Team-based Internship or Off-site Project

Gain deeper industry insights and apply what you have learnt to real-life work environments.

  • Acquire hands-on experience in analysing needs of the internship company using KE techniques

Discover Life with Us

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Internship & Placements

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Get a headstart with actual work experience under your belt.

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Career Services

Receive job placement opportunities with partner organisations.

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Teaching Staff

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